#### Overview of this book

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
R for Data Science Cookbook
Credits
www.PacktPub.com
Preface
Free Chapter
Functions in R
Data Preprocessing and Preparation
Visualizing Data with ggplot2
Making Interactive Reports
Simulation from Probability Distributions
Statistical Inference in R
Time Series Mining with R
Index

## Mining associations with the Apriori rule

Association mining is a technique that can discover interesting relationships hidden in a transaction dataset. This approach first finds all frequent itemsets and generates strong association rules from frequent itemsets. In this recipe, we will introduce how to perform association analysis using the Apriori rule.

Ensure you have completed the previous recipe by generating transactions and storing these in a variable, `trans`.

### How to do it…

Please perform the following steps to analyze association rules:

1. Use `apriori` to discover rules with support over `0.001` and confidence over `0.1`:

```> rules <- apriori(trans, parameter = list(supp = 0.001, conf = 0.1, target= "rules"))
> summary(rules)
set of 6 rules

rule length distribution (lhs + rhs):sizes
2
6

Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
2       2       2       2       2       2

summary of quality measures:
support           confidence          lift...```